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_posts/research/2021-03-03-Water_Networks.md
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teaser: assets/figures/10_water_networks_teaser.jpg
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![Leak-Mels](\assets\figures\10_water_networks_mel.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
In industrial applications, the early detection of malfunctioning factory machinery is crucial. In this paper, we consider acoustic malfunction detection via transfer learning. Contrary to the majority of current approaches which are based on deep autoencoders, we propose to extract features using neural networks that were pretrained on the task of image classification. We then use these features to train a variety of anomaly detection models and show that this improves results compared to convolutional autoencoders in recordings of four different factory machines in noisy environments. Moreover, we find that features extracted from ResNet based networks yield better results than those from AlexNet and Squeezenet. In our setting, Gaussian Mixture Models and One-Class Support Vector Machines achieve the best anomaly detection performance.
![Approach](\assets\figures\10_water_networks_approach.jpg){:style="display:block; width:40%" .align-right}
This study introduces a method for acoustic leak detection in water networks, focusing on energy efficiency and easy deployment. Utilizing recordings from microphones on a municipal water network, various anomaly detection models, both shallow and deep, were trained. The approach mimics human leak detection methods, allowing intermittent monitoring instead of constant surveillance. While detecting nearby leaks proved easy for most models, neural network-based methods excelled at identifying leaks from a distance, showcasing their effectiveness in practical applications.
{% cite muller2021acoustic %}
![Approach](\assets\figures\10_water_networks_approach.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
![Leak-Mels](\assets\figures\10_water_networks_mel.jpg){:style="display:block; width:85%" .align-center}